Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models

| openaire: EC/H2020/788489/EU//BioELCell Funding Information: This project received partial funding from the Academy of Finland via the Artificial Intelligence for Microscopic Structure Search (AIMSS) Project No. 316601 and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 788489). We are grateful for the support by the Academic Flagship programme under the FinnCERES Materials Bioeconomy and the FCAI Center for Artificial Intelligence as well as the Canada Excellence Research Chair initiative (OJR). The facilities and technical support provided by Aalto University at OtaNano – Nanomicroscopy Center (Aalto-NMC) are also acknowledged. Funding Information: Open access funding provided by Aalto University. This project received partial funding from the Academy of Finland via the Artificial Intelligence for Microscopic Structure Search (AIMSS) Project No. 316601 and the European Union’s Horizon 2020 program under the ERC Advanced Grant Agreement No. 788489, “BioElCell.” Publisher Copyright: © 2022, The Author(s).Oxidized tannic acid (OTA) is a useful biomolecule with a strong tendency to form complexes with metals and proteins. In this study we open the possibility to further the application of OTA when assembled as supramolecular systems, which typically exhibit functions that correlate with shape and associated morphological features. We used machine learning (ML) to selectively engineer OTA into particles encompassing one-dimensional to three-dimensional constructs. We employed Bayesian regression to correlate colloidal suspension conditions (pH and pKa) with the size and shape of the assembled colloidal particles. Fewer than 20 experiments were found to be sufficient to build surrogate model landscapes of OTA morphology in the experimental design space, which were chemically interpretable and endowed predictive power on data. We produced multiple property landscapes from the experimental data, helping us to infer solutions that would satisfy, simultaneously, multiple design objectives. The balance between data efficiency and the depth of information delivered by ML approaches testify to their potential to engineer particles, opening new prospects in the emerging field of particle morphogenesis, impacting bioactivity, adhesion, interfacial stabilization, and other functions inherent to OTA. Peer reviewe